40 citations · 69 across the 4 of their papers we have counts for
4 papers · 1 filter
Annual Earth-observation embeddings encode wildfire disturbance and support simplified burned area mapping
Jovana Knezevic, Clement Atzberger, Zhengpeng Feng +3
Medium-resolution (10-30 m) burned area mapping is vital for monitoring wildfires and their impacts, but remains difficult to scale. Existing methods require either curated fire-sp…
TESSERA v2: Scaling Pixel-wise Earth Foundation Models
Zhengpeng Feng, Sadiq Jaffer, Ira Shokar +12
Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial embeddings. However, how these models scale and how best to…
Scaling Up Forest Vision with Synthetic Data
Yihang She, Andrew Blake, David Coomes +1
Accurate tree segmentation is a key step in extracting individual tree metrics from forest laser scans, and is essential to understanding ecosystem functions in carbon cycling and…
Tree species classification from hyperspectral data using graph-regularized neural networks
Debmita Bandyopadhyay, Subhadip Mukherjee, James Ball +3
We propose a novel graph-regularized neural network (GRNN) algorithm for tree species classification. The proposed algorithm encompasses superpixel-based segmentation for graph con…